Effect of PEGylation on the stability of liposomes during nebulisation and in lung surfactant

Oral inhalation of anticancer drugs or drug delivery system is a novel therapeutic approach in the treatment of lung cancer and requires formulations which are sufficiently stabile during nebulisation and subsequent interaction with the surfactant lining of the lungs. In this study, we assessed the stability of plain and PEGylated transferrin-conjugated liposomes after nebulisation using two different nebulisers (i.e., air-jet and ultrasonic type). Furthermore, the integrity of the liposomal membranes was assessed after incubation in commercial lung surfactant solutions (Alveofact). All liposomal formulations showed no significant changes in their size after nebulisation, independent of the type of nebuliser or the liposomal formulation, respectively. However, PEGylation was of advantage when it came to interactions between liposomes and the surfactant lining of the lungs. PEGylated liposomes were significantly more stable and retained > 80% of their drug load over 48 h, which is more than sufficient time for the drug carriers to be taken up by transferrin receptor over-expressing cancer cells in the lung. In conclusion, PEGylated and plain Tf-conjugated liposomes are stable enough to undergo nebulisation in the course of an inhalational therapy, but PEG-stabilisation results in a higher degree of membrane integrity in lung surfactant.     

A group trust metric for identifying people of trust in online social networks

With the growth of social network services, the need for identifying trustworthy people has become a primary concern in order to protect users’ vast amounts of information from being misused by unreliable users. In this study, we propose the extended Advogato trust metric that facilitates the identification of trustworthy users associated with each individual user. By incorporating the strength of social relationships, we recursively diffuse a capacity of a target user throughout his/her personal network. Based on the capacity propagation, this paper also presents the capacity-first maximum flow method capable of finding the strongest path pertinent to discovering an ordered set of reliable users and preventing unreliable users from accessing personal networks. Experimental results demonstrate that our approach has advantages over existing representative methods in terms of both the discovery of reliable users and the preventability of unreliable users.     

Combining local branching and descent method for solving the multiple-choice knapsack problem with setups

In this paper, the multiple-choice knapsack problem with setups is tackled with an iterative method, where both local branching and descent method cooperate. First, an iterative procedure is designed for solving a series of mixed integer programming problems combined with a special reduced subproblem; that is, a combined model built by injecting some valid cardinality constraints. Second, the local branching-based learning strategy is embedded into an iterative search to mimic the variable neighborhood descent method, such that the local branching strategy drives the search process for enhancing the quality of the solutions. Third, the proposed method is experimentally analyzed on benchmark instances extracted from the literature, where its provided (lower) bounds are compared to those reached by methods published in the literature and the Cplex solver. Finally, its performance is evaluated by providing a statistical analysis.